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Optimal selection of the regularization function in a generalized total variation model. Part II: Algorithm, its analysis and numerical tests
2016, Hintermüller, Michael, Rautenberg, Carlos N., Wu, Tao, Langer, Andreas
Based on the generalized total variation model and its analysis pursued in part I (WIAS Preprint no. 2235), in this paper a continuous, i.e., infinite dimensional, projected gradient algorithm and its convergence analysis are presented. The method computes a stationary point of a regularized bilevel optimization problem for simultaneously recovering the image as well as determining a spatially distributed regularization weight. Further, its numerical realization is discussed and results obtained for image denoising and deblurring as well as Fourier and wavelet inpainting are reported on.